title: A physiology-based seizure detection system for multichannel EEG.
abstract: BACKGROUND
  Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.
  
  
  METHODS
  This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.
  
  
  RESULTS
  We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.
  
  
  CONCLUSIONS
  We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.
authors.affiliation: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
authors.forename: Chia-Ping
authors.initials: CP
authors.lastname: Shen
authors.forename: Shih-Ting
authors.initials: ST
authors.lastname: Liu
authors.forename: Wei-Zhi
authors.initials: WZ
authors.lastname: Zhou
authors.forename: Feng-Seng
authors.initials: FS
authors.lastname: Lin
authors.forename: Andy Yan-Yu
authors.initials: AY
authors.lastname: Lam
authors.forename: Hsiao-Ya
authors.initials: HY
authors.lastname: Sung
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Jeng-Wei
authors.initials: JW
authors.lastname: Lin
authors.forename: Ming-Jang
authors.initials: MJ
authors.lastname: Chiu
authors.forename: Ming-Kai
authors.initials: MK
authors.lastname: Pan
authors.forename: Jui-Hung
authors.initials: JH
authors.lastname: Kao
authors.forename: Jin-Ming
authors.initials: JM
authors.lastname: Wu
authors.forename: Feipei
authors.initials: F
authors.lastname: Lai
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D012372:ROC Curve; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D060388:Support Vector Machine; D055815:Young Adult